In an effort to develop a method that can accurately track tire tread, a group of Electrical Engineers at Duke University, in collaboration with Fetch Automotive Design Group, have utilized metallic carbon nanotubes (m-CNTs) to track changes in tire tread with 99% accuracy.
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While tire pressure sensors are already in place in most vehicles today, there is no sensor currently available to measure the tread of tires, which describes the rubber of the tire that is in contact with the road. As the tires of a vehicle continue to come in contact with the road or ground in which they are moving across, the tread, over time, becomes worn off, which can limit its traction against the road and have potential deleterious effects in regards to the consumer’s safety while driving1.
During the year of 2006, the law making legislative branch of the United States known as the U.S. Congress passed the Transportation Recall Enhancement, Accountability and Documentation (TREAD) Act, enforcing consumer safety guidelines to be mandated in all vehicles. This law required that all newly produced vehicles are equipped with a tire pressure monitoring system (TPMS) that informs the driver when their vehicle’s tire pressure becomes dangerously low2. With sensors that can be either direct or indirect, which is determined by their sensor placement either present within the wheel or the vehicle’s Antilock Braking System (ABS), the TPMS has greatly increased vehicle safety since its introduction into the market place.
In their design, the Duke Researchers placed several of the 3D printed m-CNT sensors beneath the tread, which is present within the tire, in order to detect any changes that are subjected to the tread depth of the tire. Depending entirely on the mechanics of how electric fields are capable of interacting with metallic conductors, these sensors are comprised of two small and electrically conductive electrodes. When an external oscillating electrical voltage to one of these sensors, and the other remains grounded, an electric field known as the “fringing field” is generated as a series of arcs that are present between the two electrodes3. As the tires of the vehicle continue to come in contact with the road, interference between the two electrodes is generated, which allows the sensors to measure any change in the thickness of the tire. With a sub-milimeter resolution, these sensors then notify the driver of potentially dangerous tire wear, therefore allowing them to realize when tires need to be replaced. With a capacitance change of 26 fF per millimeter thickness of rubber, the m-CNT sensors exhibited a ten fold greater sensitivity as compared to silver nanoparticle ink, a previous material used in this study by the Duke Researchers3.
The development of this sensor technology that is currently awaiting patent approval is expected to improve the health and safety of consumers, while simultaneously improving the performance of the vehicle and reducing its overall fuel consumption. In addition to their supreme sensitivity to monitor the thickness changes of tires, the m-CNTs are ideal for this application because of their durable nature that can survive potentially harsh environments in which tires are often exposed to3. While the Duke Researchers printed the m-CNTs using an aerosol jet printer, they claim that the sensors used in this study can be printed on any type of material, however, this approach may ultimately require modification when performed on a larger industrial scale. As this sensor is capable of measuring the change in thickness of a given material of interest, the Researchers are hopeful that their technology can be applied to other automotive materials such as brake pads or measuring the air pressure of tires.
References
- “Analysis of Highway Noise” C. Hogan. Water, Air and Soil Pollution. (1973). DOI: 10.1007/BF00159677.
- “Tire Pressure Monitoring System (TPMS)” – Tires Plus
- “Noninvasive Material Thickness Detection by Aerosol Jet Printed Sensors Enahnced through Metallic Carbon Nanotube Ink” J. Andrews, C. Cao, et al. IEEE Sensors Journal. (2017). DOI: 10.1109/JSEN.2017.2710085.
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